Asset Maintenance Context Mapping for Failure Correlation Detection
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Solution Overview
Problem
Existing asset management systems struggle to efficiently detect and address abnormal events across entire networks, often failing to identify related equipment that may fail or be affected, leading to increased maintenance costs and reduced efficiency.
Innovation Solution
A computer-implemented method that senses abnormal events in an equipment topology, locates abnormal assets within a context awareness map, creates an asset contextual reactive model, builds a comprehensive problem statement using machine learning models, and runs remedies to provide a recommended solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional asset management systems monitor individual assets independently, then monitoring simplicity is maintained, but failure correlation detection across the network is poor
Solution Approach 1:
The patent merges individual asset monitoring into a unified network-wide monitoring system that detects failure correlations across multiple assets. The system combines data from sensors on different assets and uses machine learning models to identify patterns and correlations that would be invisible when monitoring assets independently, thereby improving reliability through holistic network analysis.
Solution Approach 2:
The patent introduces machine learning models and analytical intermediaries that process sensor data from multiple assets to detect failure correlations. These intermediaries act as mediators between raw sensor data and maintenance decisions, enabling the system to identify hidden relationships between assets without requiring direct physical connection or complex hardwired monitoring infrastructure.
2Reliability
If comprehensive network-wide monitoring is implemented, then failure correlation detection improves, but data processing complexity increases
Solution Approach 1:
The patent segments the comprehensive data processing task into modular components: sensor data collection, preprocessing, machine learning model analysis, and maintenance decision support. Each component handles a specific aspect of data processing independently, allowing the system to manage complex network-wide monitoring through divided, manageable processing stages rather than monolithic complex processing.
Solution Approach 2:
The system employs machine learning models that automatically learn and adapt to network patterns without requiring manual programming or extensive human intervention. The models self-service by continuously processing sensor data, identifying correlations, and generating maintenance insights autonomously, reducing the complexity burden on human operators while maintaining high detection accuracy.
3Productivity
If reactive maintenance is performed only when abnormalities are detected, then maintenance costs are reduced, but mean time to repair increases
Solution Approach 1:
The patent enables preliminary maintenance actions by detecting abnormal patterns and failure correlations before actual failures occur. The machine learning models analyze sensor data to identify early signs of deterioration and predict potential failures, allowing maintenance teams to prepare and respond proactively, thereby reducing mean time to repair while maintaining cost efficiency through targeted rather than routine maintenance.
Solution Approach 2:
The system implements feedback loops where maintenance outcomes are fed back into the machine learning models to continuously improve detection accuracy. This feedback mechanism allows the system to learn from actual failures and near-misses, refining its ability to predict and prevent future failures, thereby optimizing the balance between maintenance timing and repair response time.
Data Source
AI summary
Embodiments sense an abnormal event, locate an abnormal asset within a context awareness map, build a comprehensive problem statement based on outputs from the asset contextual reactive model and a correlation and context machine learning model, the context awareness map, and data from the equipment topology, run a plurality of remedies for at least one candidate solution, and provide a recommended solution based on running the plurality of remedies for the at least one candidate solution.